Recent years have witnessed a huge breakthrough in image generation through the use of deep generative models such as Generative Adversarial Networks (GAN) [1]} and Variational Autoencoder (VAE) [2]}. Among them, GAN, including generator and discriminator, has been widely used because of its ability to generate realistic images in adversarial training methods. DCGAN [3]} combines convolutional neural network and GAN to generate realistic images throght a unsupervised method. In practical applications, we often need to generate conditional images, the proposal of CGAN[4]} solves this problem very well, which allows GAN to generated images conditionally. Pix2pixHD [5]} successfully applies GAN to image translation tasks, which can generate high resolusion images from semantic label maps by multi scale generator and discriminator. Park \(et \ al.\)  [6]} proposes a novel network structure called SPADE for image translation task, which effectively avoids "wash away" semantic information. StyleGAN [7]} successfully applied GAN to the task of face generation through a new generator architecture composed of AdaIN layers, and achieved amazing results. StyleGAN2 [8]} solves the problem of artifacts in the generated image by modifying the AdaIN structure, and further improves the quality of the generated image. However, these methods cannot be effectively applied to the task of person image generation due to the misalignment caused by pose deformation and complexity of appearance. Different from these methods, we establish dense correspondences in the deep feature space to solve the misalignment of deep features and effectively render image textures for person image generation.
